Regional Medical Campuses in Canada
Bibliographic record
Abstract
Background: Regional Medical Campuses (RMCs) are an established part of the Distributed Medical Education (DME) landscape in Canada. Combined model RMCs, offering both preclinical and clinical education have shown promising results in producing physicians who work in rural and regional settings and are currently a key avenue of expansion of medical training in Canada. Existing literature suggests that new RMCs carefully consider the communities and health systems they are a part of, and lessons learned from comparable RMCs as part of their development. Methods: We identified 4 specific domains of interest for comparing RMCs across Canada based on important elements identified in existing literature: Community, Organization, Hospitals, and Physicians. We searched high quality, publicly accessible data sources for information relevant to these domains, aggregated relevant information, and used statistical techniques to understand the range of settings for existing and proposed RMCs in Canada. Results: We found that Canadian RMCs have been deployed into a wide variety of small to medium size urban settings and have a variety of organizational profiles. RMCs were associated with 1 to 3 large hospitals, but the size of these associate hospitals also varied greatly. We found that the environments of proposed RMCs differed somewhat from existing RMCs and included examples of novel organizational constructs, settings with smaller urban population sizes, smaller hospitals, and settings with smaller and decreasing physician workforce. Discussion: The combined model RMC has proven to be a robust construct across Canada, deployed in a wide variety of different settings. Our data shows that the settings and structure of proposed new RMCs are somewhat different than existing RMCs. While the robust nature of the RMC model suggests that deployment into new settings is reasonable, the data also clearly shows areas that may be opportunities and challenges for each of these new, proposed, settings. Conclusion: There is a wealth of publicly accessible data is available about Canadian communities and health systems, which can be compiled into domains of interest for RMCs. Our study establishes a baseline data set for Canadian RMCs that will be useful for those contemplating future implementations. Proposed RMCs may be able to use this data to predict both challenges and opportunities, as well as to identify existing RMCs with similar profiles, where information exchange may be of highest value.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".